Computer vision models are being used to keep cities safer. Automated security cameras can detect crimes being committed, or monitor adherence to COVID-19 regulations, and traffic monitoring applications can reduce road accidents. In addition, AI powered monitoring use cases are also being deployed in less obvious ways.
It is important that concrete and asphalt infrastructure is maintained and kept in good condition for drivers and pedestrians. Computer vision models are being developed that can help city governments to look after roads and pavements. These systems are made possible by manual data annotation for training datasets. AI developers can access exceptional image and video annotation by working with annotation experts, like Keymakr.
Firstly, this blog will look at the challenges facing cities as they try to maintain roads and pavements. Secondly, we identify the specific ways in which AI can help to find areas in need of maintenance. And finally, we will look at the advantages provided by annotation services.
Too much to cover
City governments are responsible for thousands of miles of roads and pavements. Making sure all of this asphalt and concrete is safe to use is a daunting challenge. The scale of road and pavement maintenance demands a significant investment of resources. However, this remains an essential commitment for cities:
- Roads: Faults in road surfaces can lead to accidents, congestion and delays. Cities that experience freezing weather conditions are often badly affected when ice causes concrete to break up. Therefore, watchful maintenance is essential.
- Pavements: Faulty paving slabs and inconsistencies in asphalt can make life difficult for pedestrians. For the elderly and disabled passable pavements are important for mobility and access. Accidents caused by pavements can also be costly when cities have to pay compensation to pedestrians who fall and hurt themselves.
Finding faults with AI
Keeping roads and pavements well maintained is important. However, it can be hard for city governments to know where to allocate resources. Computer vision models can help by autonomously surveying roads and pavements and then telling maintenance crews where to target.
- Survey vehicles: AI monitoring systems can be attached to survey vehicles. Cameras supported by computer vision AI models can locate faults in roads and paving. As a result, authorities can quickly assemble an accurate picture of where to send maintenance teams.
- On-ground inspection: AI can also be used to make on the ground inspections more accurate and consistent. Computer vision models can compare road and pavement conditions to thousands of other examples instantly. They can then apply a consistent standard of assessment which can guide maintenance teams and save cities money.
Finding the right annotation help
Autonomous road and pavement condition monitoring is made possible by data annotation. By adding information to digital images, human annotators allow AI models to learn. Keymakr is an annotation provider that supports AI companies with key advantages:
- Data collection and creation: Varied data is important for model performance. However, the right data can be difficult to find in open source archives or via web scraping. Keymakr has experience collecting the right data from public sources and leverages in-house facilities to create data when needed.
- Annotation tools: Outsourcing to specialised providers allows companies to use proprietary annotation tools. Keymakr’s annotation platform is designed to accelerate annotation. In-built project management systems also ensure that labelling work is completed on time.
- Managed teams: Keymakr uses managed teams of annotators to produce exceptional datasets. By overseeing annotation work experienced managers remove a significant burden from AI companies. Centrally located teams of operators are also able to respond quickly to changing data demands and troubleshooting.